Questions answered

This notebook answers the questions provided on April 18, 2024

# Load the data 
Diet_CombinedData_Clean = readRDS(file = "~/Dropbox (Edison_Lab@UGA)/Projects/vet/Deanna/Golden_Data/CleanCombinedDietData.rds")

# Load the packages needed
library(readxl) #for loading Excel files

library(dplyr) #for data processing/cleaning

library(tidyr) #for data processing/cleaning

library(skimr) #for nice visualization of data 

library(here) #to set paths

library(DT) #nice datatables 

library(ggplot2) #for figures

library(plotly) #for interactive figures

library (openxlsx)

Plot all dogs based on study year

1. How many individual dogs were in each study year?

# Create a table that shows the frequency of the dogs enrolled by year

unique_count = Diet_CombinedData_Clean %>%  
  distinct(study_year,id) %>%  #count unique values
  group_by(study_year)%>% # count the frequency by calendar year
  summarize ("Freq" = n())%>%
  filter(!is.na(study_year))# dont include any NA

# Print the new BCS table
print(unique_count)

unique_count_datatable = datatable(unique_count, options = list(), class = "display")
unique_count_datatable

NA

Plot

# plot the frequency 
figure1 = ggplot(data=unique_count, aes(x=study_year, y=Freq, group=1)) +
  geom_line()+
  geom_point()+ 
  theme_bw()+
  theme(axis.text.x = element_text(face="bold",  size=10)) + 
  xlab("Study Year") + 
  ylab("Number of Enrolled Goldens") +
  ggtitle("Study enrollment by study year") +
  theme(
  plot.title = element_text(color="Black", size=14, face="bold"))+
  theme(plot.caption = element_text(hjust=0))
figure1

# Convert to an interactive Plotly plot
interactive_fig1 <- ggplotly(figure1)

# Print the interactive plot
interactive_fig1

BCS by study year

1. Per study year, how many of those individual dogs had a BCS of 0-3
2. Per study year how many of those individual dogs had a BCS of 4-6
3. Per study uear how many of those individual dogs had a BCS of 7-9

Diet_CombinedData_Clean$study_year <- factor(Diet_CombinedData_Clean$study_year, levels = c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"))

# Create a table for the frequency of each Body condition score range by year
BCS_table = Diet_CombinedData_Clean %>%
  distinct(study_year, bcs, id) %>%
  filter(!is.na(study_year), !is.na(bcs)) %>% # Filter out NA values in calendaryear and bcs
  mutate(BCS_Range = case_when(
    bcs >= 0 & bcs <= 3 ~ "Low BCS (0-3)",
    bcs >= 4 & bcs <= 6 ~ "Normal BCS (4-6)",
    bcs >= 7 & bcs <= 9 ~ "High BCS (7-9)",
    TRUE ~ "Unknown" # Handle any out of range or unexpected values
  )) %>%
  group_by(study_year, BCS_Range) %>%
  summarize(Freq = n(), .groups = 'drop') # Calculate frequency

# Print the new BCS table
print(BCS_table)

BCS_datatable = datatable(BCS_table, options = list(), class = "display")
BCS_datatable

# Plot the frequency using ggplot
figure2 = ggplot(data=BCS_table, aes(x=study_year, y=Freq, group=BCS_Range, color=BCS_Range)) +
  geom_line() +
  geom_point() +
  theme_bw() +
  theme(axis.text.x = element_text(face="bold", size=10)) +
  xlab("Study Year") +
  ylab("Number of Dogs") +
  ggtitle("BCS Range by Year") +
  theme(plot.title = element_text(color="Black", size=14, face="bold")) +
  labs(caption = "Figure 2: Number of Dogs with specific body condition score ranges by year.") +
  theme(plot.caption = element_text(hjust=0))

# Convert to an interactive Plotly plot
interactive_fig2 <- ggplotly(figure2)

# Print the interactive plot
interactive_fig2

Processing by study year

2. How many individual dogs were in each study year that ate:

1. Minimally processed
2. Processed
3. Ultra-processed

# Create a table for the frequency of each Body condition score range by year
processing_table = Diet_CombinedData_Clean %>%
  distinct(study_year, process.category, id)%>%  # Filter out NA values in study_year and process.category
  filter(!process.category %in% c("N/A", "UNK", "TBD")) %>%
  filter(!is.na(study_year), !is.na(process.category)) %>% # Filter out NA values in calendaryear and bcs
  group_by(study_year, process.category) %>%
  summarize(Freq = n(), .groups = 'drop') # Calculate frequency

# Print the new BCS table
print(processing_table)

processing_datatable = datatable(processing_table, options = list(), class = "display")
processing_datatable

# Plot the frequency using ggplot
figure3 = ggplot(data=processing_table, aes(x=study_year, y=Freq, group=process.category, color=process.category)) +
  geom_line() +
  geom_point() +
  theme_bw() +
  theme(axis.text.x = element_text(face="bold", size=10)) +
  xlab("Study Year") +
  ylab("Number of Dogs") +
  ggtitle("Processing Categorization by Study Year") +
  theme(plot.title = element_text(color="Black", size=14, face="bold")) +
  labs(caption = "Figure 2: Number of Dogs with specific body condition score ranges by year.") +
  theme(plot.caption = element_text(hjust=0))

# Convert to an interactive Plotly plot
interactive_fig3 <- ggplotly(figure3)

# Print the interactive plot
interactive_fig3

Cardiovascular

#load data
project.path = here()

Combined_Data.location = here::here("1_Data","Raw_data","Diagnosis","cardiovascular.csv")

# load the data 

cardiovascular = read.csv(Combined_Data.location)

#make all the names lowercase
names(cardiovascular)<-tolower(names(cardiovascular))

#add unique IDs
cardiovascular$unique_id = paste(cardiovascular$id, "_", cardiovascular$study_year)

#relocate to begining

cardiovascular = cardiovascular %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(cardiovascular)

#skimr::skim(cardiovascular)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(cardiovascular$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(cardiovascular$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
cardiovascular = cardiovascular %>% 
 rename("any_cardio" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
cardiovascular_reduced <- cardiovascular %>% 
  select(unique_id, any_cardio)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombinedData_Clean %>%
  left_join(cardiovascular_reduced, by = "unique_id")

Endocrine

#load data
project.path = here()

Combined_Data.location = here::here("1_Data","Raw_data","Diagnosis","endocrine.csv")

# load the data 

endocrine = read.csv(Combined_Data.location)

#make all the names lowercase
names(endocrine)<-tolower(names(endocrine))

#add unique IDs
endocrine$unique_id = paste(endocrine$id, "_", endocrine$study_year)

#relocate to begining

endocrine = endocrine %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(endocrine)

#skimr::skim(endocrine)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(endocrine$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(endocrine$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
endocrine = endocrine %>% 
 rename("any_endocrine" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
endocrine_reduced <- endocrine %>% 
  select(unique_id, any_endocrine)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(endocrine_reduced, by = "unique_id")

Gastrointestinal

#load data
project.path = here()

Data.location = here::here("1_Data","Raw_data","Diagnosis","gastrointestinal.csv")

# load the data 

gastrointestinal = read.csv(Data.location)

#make all the names lowercase
names(gastrointestinal)<-tolower(names(gastrointestinal))

#add unique IDs
gastrointestinal$unique_id = paste(gastrointestinal$id, "_", gastrointestinal$study_year)

#relocate to begining

gastrointestinal = gastrointestinal %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(gastrointestinal)

#skimr::skim(gastrointestinal)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(gastrointestinal$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(gastrointestinal$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
gastrointestinal = gastrointestinal %>% 
 rename("any_gastrointestinal" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
gastrointestinal_reduced <- gastrointestinal %>% 
  select(unique_id, any_gastrointestinal)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(gastrointestinal_reduced, by = "unique_id")

Hematologic

#load data
project.path = here()

Data.location = here::here("1_Data","Raw_data","Diagnosis","hematologic.csv")

# load the data 

hematologic = read.csv(Data.location)

#make all the names lowercase
names(hematologic)<-tolower(names(hematologic))

#add unique IDs
hematologic$unique_id = paste(hematologic$id, "_", hematologic$study_year)

#relocate to begining

hematologic = hematologic %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(hematologic)

#skimr::skim(hematologic)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(hematologic$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(hematologic$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
hematologic = hematologic %>% 
 rename("any_hematologic" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
hematologic_reduced <- hematologic %>% 
  select(unique_id, any_hematologic)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(hematologic_reduced, by = "unique_id")

musculoskeletal

#load data
project.path = here()

Data.location = here::here("1_Data","Raw_data","Diagnosis","musculoskeletal.csv")

# load the data 

musculoskeletal = read.csv(Data.location)

#make all the names lowercase
names(musculoskeletal)<-tolower(names(musculoskeletal))

#add unique IDs
musculoskeletal$unique_id = paste(musculoskeletal$id, "_", musculoskeletal$study_year)

#relocate to begining

musculoskeletal = musculoskeletal %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(musculoskeletal)

#skimr::skim(musculoskeletal)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(musculoskeletal$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(musculoskeletal$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
musculoskeletal = musculoskeletal %>% 
 rename("any_musculoskeletal" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
musculoskeletal_reduced <- musculoskeletal %>% 
  select(unique_id, any_musculoskeletal)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(musculoskeletal_reduced, by = "unique_id")

Nervous

#load data
project.path = here()

Data.location = here::here("1_Data","Raw_data","Diagnosis","nervous.csv")

# load the data 

nervous = read.csv(Data.location)

#make all the names lowercase
names(nervous)<-tolower(names(nervous))

#add unique IDs
nervous$unique_id = paste(nervous$id, "_", nervous$study_year)

#relocate to begining

nervous = nervous %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(nervous)

#skimr::skim(nervous)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(nervous$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(nervous$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
nervous = nervous %>% 
 rename("any_nervous" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
nervous_reduced <- nervous %>% 
  select(unique_id, any_nervous)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(nervous_reduced, by = "unique_id")

Urinary

#load data
project.path = here()

Data.location = here::here("1_Data","Raw_data","Diagnosis","urinary.csv")

# load the data 

urinary = read.csv(Data.location)

#make all the names lowercase
names(urinary)<-tolower(names(urinary))

#add unique IDs
urinary$unique_id = paste(urinary$id, "_", urinary$study_year)

#relocate to begining

urinary = urinary %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(urinary)

#skimr::skim(urinary)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(urinary$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(urinary$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
urinary = urinary %>% 
 rename("any_urinary" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
urinary_reduced <- urinary %>% 
  select(unique_id, any_urinary)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(urinary_reduced, by = "unique_id")

add column to determine if a dog has any major diagnosis

Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  rowwise() %>%  # Apply the operation row by row
  mutate(any_major_diagnosis = if_else(any_cardio == 1 | any_endocrine == 1 | any_gastrointestinal == 1 | any_musculoskeletal == 1 | any_nervous == 1 | any_urinary == 1 | any_hematologic == 1, 1, 0)) %>%
  ungroup()  # Remove the rowwise grouping

minor diagnosis

Eye

#load data
project.path = here()

Data.location = here::here("1_Data","Raw_data","Diagnosis","eye.csv")

# load the data 

eye = read.csv(Data.location)

#make all the names lowercase
names(eye)<-tolower(names(eye))

#add unique IDs
eye$unique_id = paste(eye$id, "_", eye$study_year)

#relocate to begining

eye = eye %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(eye)

#skimr::skim(eye)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(eye$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(eye$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
eye = eye %>% 
 rename("any_eye" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
eye_reduced <- eye %>% 
  select(unique_id, any_eye)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(eye_reduced, by = "unique_id")

Infection

#load data
project.path = here()

Data.location = here::here("1_Data","Raw_data","Diagnosis","infectious.csv")

# load the data 

infectious = read.csv(Data.location)

#make all the names lowercase
names(infectious)<-tolower(names(infectious))

#add unique IDs
infectious$unique_id = paste(infectious$id, "_", infectious$study_year)

#relocate to begining

infectious = infectious %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(infectious)

#skimr::skim(infectious)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(infectious$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(infectious$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
infectious = infectious %>% 
 rename("any_infectious" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
infectious_reduced <- infectious %>% 
  select(unique_id, any_infectious)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(infectious_reduced, by = "unique_id")

skin

#load data
project.path = here()

Data.location = here::here("1_Data","Raw_data","Diagnosis","skin.csv")

# load the data 

skin = read.csv(Data.location)

#make all the names lowercase
names(skin)<-tolower(names(skin))

#add unique IDs
skin$unique_id = paste(skin$id, "_", skin$study_year)

#relocate to begining

skin = skin %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(skin)

#skimr::skim(skin)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(skin$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(skin$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
skin = skin %>% 
 rename("any_skin" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
skin_reduced <- skin %>% 
  select(unique_id, any_skin)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(skin_reduced, by = "unique_id")

reproductive

#load data
project.path = here()

Data.location = here::here("1_Data","Raw_data","Diagnosis","reproductive.csv")

# load the data 

reproductive = read.csv(Data.location)

#make all the names lowercase
names(reproductive)<-tolower(names(reproductive))

#add unique IDs
reproductive$unique_id = paste(reproductive$id, "_", reproductive$study_year)

#relocate to begining

reproductive = reproductive %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(reproductive)

#skimr::skim(reproductive)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(reproductive$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(reproductive$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
reproductive = reproductive %>% 
 rename("any_reproductive" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
reproductive_reduced <- reproductive %>% 
  select(unique_id, any_reproductive)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(reproductive_reduced, by = "unique_id")

dental

#load data
project.path = here()

Data.location = here::here("1_Data","Raw_data","Diagnosis","dental.csv")

# load the data 

dental = read.csv(Data.location)

#make all the names lowercase
names(dental)<-tolower(names(dental))

#add unique IDs
dental$unique_id = paste(dental$id, "_", dental$study_year)

#relocate to begining

dental = dental %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(dental)

#skimr::skim(dental)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(dental$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(dental$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
dental = dental %>% 
 rename("any_dental" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
dental_reduced <- dental %>% 
  select(unique_id, any_dental)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(dental_reduced, by = "unique_id")

ear nose throat

#load data
project.path = here()

Data.location = here::here("1_Data","Raw_data","Diagnosis","ear_nose_throat.csv")

# load the data 

ear_nose_throat = read.csv(Data.location)

#make all the names lowercase
names(ear_nose_throat)<-tolower(names(ear_nose_throat))

#add unique IDs
ear_nose_throat$unique_id = paste(ear_nose_throat$id, "_", ear_nose_throat$study_year)

#relocate to begining

ear_nose_throat = ear_nose_throat %>%

  relocate(unique_id, .before = id)

#Check 

#dplyr::glimpse(ear_nose_throat)

#skimr::skim(ear_nose_throat)

# Check study ID

# According to our data source, there were 3044 dogs apart of the study

Unique_Identifiers = unique(ear_nose_throat$id)

length(Unique_Identifiers)

[1] 3044

#check study Year

# according to our data souce, dogs can be apart of the study for a maximum of 10 years, the bsaeline is 0 and should be in whole number

(unique(ear_nose_throat$study_year))

 [1]  0  1  2  3  4  5  6  7  8  9 10

#rename any column
ear_nose_throat = ear_nose_throat %>% 
 rename("any_ear_nose_throat" = "any")

Add to diet data

# Select only the unique_id and any columns from cardiovascular
ear_nose_throat_reduced <- ear_nose_throat %>% 
  select(unique_id, any_ear_nose_throat)

# Joining the reduced cardiovascular data 
Diet_CombineData_Updated <- Diet_CombineData_Updated %>%
  left_join(ear_nose_throat_reduced, by = "unique_id")

add column to determine if a dog has any minor diagnosis

Diet_CombineData_Updated = Diet_CombineData_Updated %>%
  rowwise() %>%  # Apply the operation row by row
  mutate(any_minor_diagnosis = if_else(any_eye == 1 | any_dental == 1 | any_ear_nose_throat == 1 | any_reproductive == 1 | any_infectious == 1 | any_skin == 1, 1, 0)) %>%
  ungroup()  # Remove the rowwise grouping

add column to determine if a dog has any diagnosis

Diet_CombineData_Updated = Diet_CombineData_Updated %>%
  rowwise() %>%  # Apply the operation row by row
  mutate(any_diagnosis = if_else(any_cardio == 1 | any_endocrine == 1 | any_gastrointestinal == 1 | any_musculoskeletal == 1 | any_nervous == 1 | any_nervous == 1 | any_urinary == 1 | any_eye == 1 | any_ear_nose_throat == 1 | any_infectious == 1 | any_skin == 1 | any_urinary ==1, 1, 0)) %>%
  ungroup()  # Remove the rowwise grouping

Major minor and diagnosis

# Summarize data to get counts of diagnosis vs. no diagnosis by study_year
#minor_diagnosis_summary <- Diet_CombineData_Updated %>%
#   filter(!is.na(any_minor_diagnosis)) %>%
#  group_by(study_year, any_minor_diagnosis) %>%
#  summarize(Frequency = n(), .groups = 'drop') %>%
#  mutate(any_minor_diagnosis = factor(any_minor_diagnosis, levels = c(0, 1), labels = c("No Diagnosis", "Diagnosis")))
#minor_diagnosis_summary


# Update the table to include BCS ranges
minor_diagnosis_summary <- Diet_CombineData_Updated %>%
  filter(!is.na(any_minor_diagnosis)) %>%
  filter(!is.na(bcs)) %>%
  mutate(BCS_Range = case_when(
    bcs >= 0 & bcs <= 3 ~ "Low BCS (0-3)",
    bcs >= 4 & bcs <= 6 ~ "Normal BCS (4-6)",
    bcs >= 7 & bcs <= 9 ~ "High BCS (7-9)",
    TRUE ~ "Unknown" 
  )) %>%
  group_by(study_year, BCS_Range, any_minor_diagnosis) %>%
  summarize(Frequency = n(), .groups = 'drop') %>%
  mutate(any_minor_diagnosis = factor(any_minor_diagnosis, levels = c(0, 1), labels = c("No Diagnosis", "Diagnosis")))

# Print the updated summary
print(minor_diagnosis_summary)

minor_diagnosis_summary_datatable = datatable(minor_diagnosis_summary, options = list(), class = "display")
minor_diagnosis_summary_datatable

# Summarize data to get counts of diagnosis vs. no diagnosis by study_year
#major_diagnosis_summary <- Diet_CombineData_Updated %>%
#   filter(!is.na(any_major_diagnosis)) %>%
#  group_by(study_year, any_major_diagnosis) %>%
#  summarize(Frequency = n(), .groups = 'drop') %>%
#  mutate(any_major_diagnosis = factor(any_major_diagnosis, levels = c(0, 1), labels = c("No Diagnosis", "Diagnosis")))
#major_diagnosis_summary


# Update the table to include BCS ranges
major_diagnosis_summary <- Diet_CombineData_Updated %>%
  filter(!is.na(any_major_diagnosis)) %>%
  filter(!is.na(bcs)) %>%
  mutate(BCS_Range = case_when(
    bcs >= 0 & bcs <= 3 ~ "Low BCS (0-3)",
    bcs >= 4 & bcs <= 6 ~ "Normal BCS (4-6)",
    bcs >= 7 & bcs <= 9 ~ "High BCS (7-9)",
    TRUE ~ "Unknown" 
  )) %>%
  group_by(study_year, BCS_Range, any_major_diagnosis) %>%
  summarize(Frequency = n(), .groups = 'drop') %>%
  mutate(any_major_diagnosis = factor(any_major_diagnosis, levels = c(0, 1), labels = c("No Diagnosis", "Diagnosis")))

# Print the updated summary
print(major_diagnosis_summary)

major_diagnosis_summary_datatable = datatable(major_diagnosis_summary, options = list(), class = "display")
major_diagnosis_summary_datatable

# Any diagnosis

# Update the table to include BCS ranges
diagnosis_summary <- Diet_CombineData_Updated %>%
  filter(!is.na(any_diagnosis)) %>%
  filter(!is.na(bcs)) %>%
  mutate(BCS_Range = case_when(
    bcs >= 0 & bcs <= 3 ~ "Low BCS (0-3)",
    bcs >= 4 & bcs <= 6 ~ "Normal BCS (4-6)",
    bcs >= 7 & bcs <= 9 ~ "High BCS (7-9)",
    TRUE ~ "Unknown" 
  )) %>%
  group_by(study_year, BCS_Range, any_diagnosis) %>%
  summarize(Frequency = n(), .groups = 'drop') %>%
  mutate(any_diagnosis = factor(any_diagnosis, levels = c(0, 1), labels = c("No Diagnosis", "Diagnosis")))

# Print the updated summary
print(diagnosis_summary)

diagnosis_summary_datatable = datatable(diagnosis_summary, options = list(), class = "display")
diagnosis_summary_datatable

# Create the bar plot
major_plot <- ggplot(major_diagnosis_summary, aes(x = study_year, y = Frequency, fill = any_major_diagnosis, group = )) +
  geom_bar(stat = "identity", position = "dodge") + facet_wrap(~BCS_Range)+ # Use dodge position to place bars side by side
  labs(title = "Frequency of  Major Diagnosis by Study Year",
       x = "Study Year",
       y = "Frequency",
       fill = "Diagnosis Status") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

# Display the plot
print(major_plot)

# Convert to an interactive Plotly plot
interactive_fig4 <- ggplotly(major_plot)

interactive_fig4 <- interactive_fig4 %>%
  layout(legend = list(orientation = "h", x = 0.5, xanchor = "center", y = 1.1))

# Print the interactive plot
interactive_fig4

# Create the bar plot
minor_plot <- ggplot(minor_diagnosis_summary, aes(x = study_year, y = Frequency, fill = any_minor_diagnosis)) +
  geom_bar(stat = "identity", position = "dodge") + facet_wrap(~BCS_Range) +  # Use dodge position to place bars side by side
  labs(title = "Frequency of Minor Diagnosis by Study Year ",
       x = "Study Year",
       y = "Frequency",
       fill = "Diagnosis Status") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "top")  # Rotate x-axis labels for readability

# Display the plot
print(minor_plot)

# Convert to an interactive Plotly plot
interactive_fig5 <- ggplotly(minor_plot)


interactive_fig5 <- interactive_fig5 %>%
  layout(legend = list(orientation = "h", x = 0.5, xanchor = "center", y = 1.1))

# Print the interactive plot
interactive_fig5

NA

# Create the bar plot
diagnosis_plot <- ggplot(diagnosis_summary, aes(x = study_year, y = Frequency, fill = any_diagnosis, group = )) +
  geom_bar(stat = "identity", position = "dodge") + facet_wrap(~BCS_Range)+ # Use dodge position to place bars side by side
  labs(title = "Frequency of Diagnosis by Study Year",
       x = "Study Year",
       y = "Frequency",
       fill = "Diagnosis Status") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

# Display the plot
print(major_plot)

# Convert to an interactive Plotly plot
interactive_fig6 <- ggplotly(diagnosis_plot)

interactive_fig6 <- interactive_fig6 %>%
  layout(legend = list(orientation = "h", x = 0.5, xanchor = "center", y = 1.1))

# Print the interactive plot
interactive_fig6

Filter the dogs based on Age, bcs, and diagnosis

Determine the number of dogs that have a BCS of 4,5, or 6 between the age of 0-3, 4-6 , 7+ that are generally healthy on a minimally processed diet

# Filter Diet_Data_Clean for age and BCS conditions
#eligible_young_dogs <- Diet_Data_Clean %>%
#  filter(visit.age < 3, BCS_numeric < 7)
#length(unique(eligible_young_dogs$id))

# Filter Diet_Data_Clean for age under 3 years and BCS below 7
#eligible_young_dogs <- Diet_CombineData_Updated %>%
##  filter(visit.age < 3, bcs %in% c(4,5,6)) %>%
#  distinct()
#length(unique(eligible_young_dogs$id))

eligible_young_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age <= 3,                # Age under 3 years
         bcs %in% c(4, 5, 6),          # BCS of 4, 5, or 6
         any_major_diagnosis == 0,     # Exclude dogs with any major diagnosis
         process.category == "Minimally Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_young_dogs$id))

[1] 176

eligible_mid_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age >3 & visit.age <= 6,                # between 4-6
         bcs %in% c(4, 5, 6),          # BCS of 4, 5, or 6
         any_major_diagnosis == 0,     # Exclude dogs with any major diagnosis
         process.category == "Minimally Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_mid_dogs$id))

[1] 169

eligible_old_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age > 7,                # over 7
         bcs %in% c(4, 5, 6),          # BCS of 4, 5, or 6
         any_major_diagnosis == 0,     # Exclude dogs with any major diagnosis
         process.category == "Minimally Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_old_dogs$id))

[1] 92

# Get unique IDs from each table
old_ids <- distinct(eligible_old_dogs, id)
mid_ids <- distinct(eligible_mid_dogs, id)
young_ids <- distinct(eligible_young_dogs, id)

# Find common ids across all three tables
common_ids <- intersect(intersect(old_ids$id, mid_ids$id), young_ids$id)
# Find common IDs across all three tables using reduce and intersect
#common_ids <- Reduce(intersect, list(old_ids$id, mid_ids$id, young_ids$id))


eligible_dogs <- bind_rows(
  filter(eligible_old_dogs, id %in% common_ids),
  filter(eligible_mid_dogs, id %in% common_ids),
  filter(eligible_young_dogs, id %in% common_ids)
)


eligible_dogs1_datatable = datatable(eligible_dogs, options = list(), class = "display")
eligible_dogs1_datatable

length(unique(eligible_dogs$id))

[1] 49

Determine the number of dogs that have a BCS of 4,5, or 6 between the age of 0-3, 4-6 , 7+ that are generally healthy on a ultra processed diet

# Filter Diet_Data_Clean for age and BCS conditions
#eligible_young_dogs <- Diet_Data_Clean %>%
#  filter(visit.age < 3, BCS_numeric < 7)
#length(unique(eligible_young_dogs$id))

# Filter Diet_Data_Clean for age under 3 years and BCS below 7
#eligible_young_dogs <- Diet_CombineData_Updated %>%
##  filter(visit.age < 3, bcs %in% c(4,5,6)) %>%
#  distinct()
#length(unique(eligible_young_dogs$id))

eligible_young_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age <= 3,                # Age under 3 years
         bcs %in% c(4, 5, 6),          # BCS of 4, 5, or 6
         any_major_diagnosis == 0,     # Exclude dogs with any major diagnosis
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_young_dogs$id))

[1] 2340

eligible_mid_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age >3 & visit.age <= 6,                # between 4-6
         bcs %in% c(4, 5, 6),          # BCS of 4, 5, or 6
         any_major_diagnosis == 0,     # Exclude dogs with any major diagnosis
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_mid_dogs$id))

[1] 2155

eligible_old_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age > 7,                # over 7
         bcs %in% c(4, 5, 6),          # BCS of 4, 5, or 6
         any_major_diagnosis == 0,     # Exclude dogs with any major diagnosis
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_old_dogs$id))

[1] 1453

# Get unique IDs from each table
old_ids <- distinct(eligible_old_dogs, id)
mid_ids <- distinct(eligible_mid_dogs, id)
young_ids <- distinct(eligible_young_dogs, id)

# Find common ids across all three tables
common_ids <- intersect(intersect(old_ids$id, mid_ids$id), young_ids$id)
# Find common IDs across all three tables using reduce and intersect
#common_ids <- Reduce(intersect, list(old_ids$id, mid_ids$id, young_ids$id))


eligible_dogs <- bind_rows(
  filter(eligible_old_dogs, id %in% common_ids),
  filter(eligible_mid_dogs, id %in% common_ids),
  filter(eligible_young_dogs, id %in% common_ids)
)
eligible_dogs

#eligible_dogs1_datatable = datatable(eligible_dogs, options = list(), class = "display")
#eligible_dogs1_datatable
length(unique(eligible_dogs$id))

[1] 1078

length(unique(eligible_dogs$id))

[1] 1078

write.xlsx(eligible_dogs, file = "EligibleDogs.xlsx", sheetName = "Data", rowNames = FALSE)

Filter the dogs based on Age, bcs, and diagnosis

# Filter Diet_Data_Clean for age and BCS conditions
#eligible_young_dogs <- Diet_Data_Clean %>%
#  filter(visit.age < 3, BCS_numeric < 7)
#length(unique(eligible_young_dogs$id))

# Filter Diet_Data_Clean for age under 3 years and BCS below 7
#eligible_young_dogs <- Diet_CombineData_Updated %>%
##  filter(visit.age < 3, bcs %in% c(4,5,6)) %>%
#  distinct()
#length(unique(eligible_young_dogs$id))

eligible_young_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age <= 3,                # Age under 3 years
         bcs %in% c(4, 5, 6),          # BCS of 4, 5, or 6
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_young_dogs$id))

[1] 2737

eligible_mid_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age >3 & visit.age <= 6,                # between 4-6
         bcs %in% c(4, 5, 6),          # BCS of 4, 5, or 6
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_mid_dogs$id))

[1] 2352

eligible_old_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age > 7,                # over 7
         bcs %in% c(4, 5, 6),          # BCS of 4, 5, or 6
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_old_dogs$id))

[1] 1734

# Get unique IDs from each table
old_ids <- distinct(eligible_old_dogs, id)
mid_ids <- distinct(eligible_mid_dogs, id)
young_ids <- distinct(eligible_young_dogs, id)

# Find common ids across all three tables
common_ids <- intersect(intersect(old_ids$id, mid_ids$id), young_ids$id)
# Find common IDs across all three tables using reduce and intersect
#common_ids <- Reduce(intersect, list(old_ids$id, mid_ids$id, young_ids$id))


eligible_dogs <- bind_rows(
  filter(eligible_old_dogs, id %in% common_ids),
  filter(eligible_mid_dogs, id %in% common_ids),
  filter(eligible_young_dogs, id %in% common_ids)
)


#eligible_dogs1_datatable = datatable(eligible_dogs, options = list(), class = "display")
#eligible_dogs1_datatable
length(unique(eligible_dogs$id))

[1] 1561

# Filter Diet_Data_Clean for age and BCS conditions
#eligible_young_dogs <- Diet_Data_Clean %>%
#  filter(visit.age < 3, BCS_numeric < 7)
#length(unique(eligible_young_dogs$id))

# Filter Diet_Data_Clean for age under 3 years and BCS below 7
#eligible_young_dogs <- Diet_CombineData_Updated %>%
##  filter(visit.age < 3, bcs %in% c(4,5,6)) %>%
#  distinct()
#length(unique(eligible_young_dogs$id))

eligible_young_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age <= 3,                # Age under 3 years
         bcs %in% c(7, 8, 9),          # BCS of 4, 5, or 6
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_young_dogs$id))

[1] 583

eligible_mid_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age >3 & visit.age <= 6,                # between 4-6
         bcs %in% c(7, 8, 9),          # BCS of 4, 5, or 6
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_mid_dogs$id))

[1] 615

eligible_old_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age > 7,                # over 7
         bcs %in% c(7, 8, 9),          # BCS of 4, 5, or 6
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_old_dogs$id))

[1] 451

# Get unique IDs from each table
old_ids <- distinct(eligible_old_dogs, id)
mid_ids <- distinct(eligible_mid_dogs, id)
young_ids <- distinct(eligible_young_dogs, id)

# Find common ids across all three tables
common_ids <- intersect(intersect(old_ids$id, mid_ids$id), young_ids$id)
# Find common IDs across all three tables using reduce and intersect
#common_ids <- Reduce(intersect, list(old_ids$id, mid_ids$id, young_ids$id))


eligible_dogs <- bind_rows(
  filter(eligible_old_dogs, id %in% common_ids),
  filter(eligible_mid_dogs, id %in% common_ids),
  filter(eligible_young_dogs, id %in% common_ids)
)


eligible_dogs3_datatable = datatable(eligible_dogs, options = list(), class = "display")
eligible_dogs3_datatable

length(unique(eligible_dogs$id))

[1] 96

# Filter Diet_Data_Clean for age and BCS conditions
#eligible_young_dogs <- Diet_Data_Clean %>%
#  filter(visit.age < 3, BCS_numeric < 7)
#length(unique(eligible_young_dogs$id))

# Filter Diet_Data_Clean for age under 3 years and BCS below 7
#eligible_young_dogs <- Diet_CombineData_Updated %>%
##  filter(visit.age < 3, bcs %in% c(4,5,6)) %>%
#  distinct()
#length(unique(eligible_young_dogs$id))

eligible_young_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age <= 3,                # Age under 3 years
         bcs %in% c(4, 5, 6),          # BCS of 4, 5, or 6
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_young_dogs$id))

[1] 2737

eligible_mid_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age >3 & visit.age <= 6,                # between 4-6
         bcs %in% c(7, 8, 9),          # BCS of 7 8 or 9
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_mid_dogs$id))

[1] 615

eligible_old_dogs <- Diet_CombineData_Updated %>%
  filter(visit.age > 7,                # over 7
         bcs %in% c(7, 8, 9),          # BCS of 7 8 or 9
         process.category == "Ultra Processed") %>%
  distinct()  # Remove duplicates
length(unique(eligible_old_dogs$id))

[1] 451

# Get unique IDs from each table
old_ids <- distinct(eligible_old_dogs, id)
mid_ids <- distinct(eligible_mid_dogs, id)
young_ids <- distinct(eligible_young_dogs, id)

# Find common ids across all three tables
common_ids <- intersect(intersect(old_ids$id, mid_ids$id), young_ids$id)
# Find common IDs across all three tables using reduce and intersect
#common_ids <- Reduce(intersect, list(old_ids$id, mid_ids$id, young_ids$id))


eligible_dogs <- bind_rows(
  filter(eligible_old_dogs, id %in% common_ids),
  filter(eligible_mid_dogs, id %in% common_ids),
  filter(eligible_young_dogs, id %in% common_ids)
)


eligible_dogs4_datatable = datatable(eligible_dogs, options = list(), class = "display")
eligible_dogs4_datatable

length(unique(eligible_dogs$id))

[1] 213

# Summarize the data to get counts of diagnoses by study_year
cardio_summary <- Diet_CombineData_Updated %>%
   filter(!is.na(any_cardio)) %>%
  group_by(study_year, any_cardio) %>%
  summarize(Frequency = n(), .groups = 'drop')

# Ensure the 'any' column is a factor for better plotting
cardio_summary$any_cardio <- factor(cardio_summary$any_cardio, levels = c(0, 1), labels = c("No Diagnosis", "Diagnosis"))


cardio_summary_datatable = datatable(cardio_summary, options = list(), class = "display")
cardio_summary_datatable

# Create the bar plot
cardio_plot <- ggplot(cardio_summary, aes(x = study_year, y = Frequency, fill = any_cardio)) +
  geom_bar(stat = "identity", position = "dodge") + # 'dodge' position to place bars side by side
  labs(title = "Frequency of Cardiovascular Diagnoses by Study Year",
       x = "Study Year",
       y = "Number of Dogs",
       fill = "Cardio Diagnosis") +
  scale_fill_brewer(palette = "Set1") + # Optional: Adds color
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "top") # Improve readability of x-axis labels

# Display the plot
print(cardio_plot)

# Convert to an interactive Plotly plot
interactive_fig4 <- ggplotly(cardio_plot)

# Print the interactive plot
interactive_fig4

NA

save(interactive_fig1,interactive_fig2,interactive_fig3,interactive_fig4,interactive_fig5,interactive_fig6, file = "~/Dropbox (Edison_Lab@UGA)/Projects/vet/Deanna/Golden_Data/figures4.RData")


save(unique_count_datatable, BCS_datatable, processing_datatable,eligible_dogs1_datatable,eligible_dogs3_datatable,eligible_dogs4_datatable,minor_diagnosis_summary_datatable,major_diagnosis_summary_datatable
,diagnosis_summary_datatable, file = "~/Dropbox (Edison_Lab@UGA)/Projects/vet/Deanna/Golden_Data/tables4.RData")